Data and Functions
Packages and Functions
library(Rcpp)
library(ggplot2)
library(plotly)
library(tools)
library(data.table)
library(ggpubr)
library(vegan)
# sourceCpp('src/fasta_seq_names.cpp')
# sourceCpp('src/primer_map.cpp')Data Files
metadata <- fread('data/DARTE-QM-v3-metadata-fromKathyMou.csv')
metadata[[1]] <- gsub('\\.', '_', metadata[[1]])
categories <- fread('data/category_all.tsv', header = FALSE)
colnames(categories) <- c('Source', 'ARG_Class', 'Primer')
BLAST_table <- readRDS('data/BLAST_table.rds')
expected_mock <- readRDS('data/expected_mock.rds')
primers <- readRDS('data/primers.rds')
read_counts <- readRDS('data/read_counts.rds')
read_counts_unexpected <- readRDS('data/read_counts_unexpected.rds')
read_counts_unexpected <- read_counts_unexpected[!(read_counts_unexpected[[1]] %in% read_counts[[1]])]
spike_counts <- readRDS('data/spike_counts.rds')
BLAST_matrix <- readRDS('data/blast_matrix.rds')
groups <- cbind(metadata$Sample_Name, factor(metadata$Test_for))
soil_column_I_ARGS <- readRDS('data/soil_column_I_ARGs.RDS')Sequencing Results
Percent Identity
This is created by averaging the number of reads from all samples from a single source, i.e. the average number of reads aligned from soil at each percent identity threshold.
Relative Abundance
This is the same information as the Percent Identity graph, but represented as the proportion of the total number of reads from the raw FASTQ files.
Percent Aligned
This is the same information as the Percent Identity graph, but represented as the proportion of the total number of aligned reads. I.E. at each increasing percent identity, the number of reads aligned is divided by the number of reads aligned at 60% (the total number of aligned reads).
Normality
We expect qPCR data to be non-normal. If it were normally distributed, I would suspect that the number of primers was affecting the results more than the gene quantity.
98% Identity match for reads of length >= 100 bp seems to be the ideal threshold for number of reads captured compared to confidence in alignment. For subsequent analyses, I used that threshold for determining identified ARGs.
Mock Samples - ARG Amplicon Detection
Each primer/ARG genes presented is expected to be found based on the mock.expected.fa file. 75% of the primers expected to bind genes in the mock samples were successful, and detected in the sequencing. 87% of the ARG genes expected to be found in the mock samples were found.
ARGs in Mock
BLAST against mock.expected.fa, compare to mock.expected.fa. Looking to see how many of the known ARGs are detected. Something is wrong with Q3_Mock_0_025spike_C.
By Primer
Same data, but by individual primer pressence.
Spike Detection
Bar Graphs
Linear Relation
Sample Ordination
Non-Normalized PCA
Distances were calculated from raw read counts to compare to how normalizing with relative abundance clusters replicates. Without housekeeping genes, we can’t directly compare samples to each other in terms of abundance or expression of certain genes. With relative abundance though, we should be able to compare the ratios.
Normalized PCA
Using relative abundance to normalize samples clusters the replicates tighter, indicating that it works well overall as a normalization method.
NMDS
PCA - Test
PCA - Matrix
Hierarchical Clustering
Bray-Curtis
Jaccard
Euclidean
Gower
K-Means Clustering
Optimize K
K(4)
K(7)
K(13)
Sample Compositon
ARG Profile
NGS vs PCR
